ArticleBiomedical optics express2020
Automatic recognition of breast invasive ductal carcinoma based on terahertz spectroscopy with wavelet packet transform and machine learning.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Terahertz spectral imaging for early assessment of frostbite injuries using the double Debye model and supervised machine learning.Biomedical optics express · 2026Article
- Digital breast tomosynthesis-based radiomics for prediction of prognosis in breast cancer: a multicenter study.Gland surgery · 2026Article
- Deep learning-assisted terahertz intelligent detection and identification of cancer tissue.Fundamental research · 2025Article
- Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast.PloS one · 2025Article
- Article
- Biomedical application of terahertz imaging technology: a narrative review.Quantitative imaging in medicine and surgery · 2023Review
- Identifying plastics with photoluminescence spectroscopy and machine learning.Scientific reports · 2022Article
- Accurate and early prediction of the wound healing outcome of burn injuries using the wavelet Shannon entropy of terahertz time-domain waveforms.Journal of biomedical optics · 2022Article
- A General Neural Network Model for Complex Refractive Index Extraction of Low-Loss Materials in the Transmission-Mode THz-TDS.Sensors (Basel, Switzerland) · 2022Article
- Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images.Diagnostics (Basel, Switzerland) · 2022Article
- Deep neural network classification of in vivo burn injuries with different etiologies using terahertz time-domain spectral imaging.Biomedical optics express · 2022Article
- Supervised machine learning for automatic classification of in vivo scald and contact burn injuries using the terahertz Portable Handheld Spectral Reflection (PHASR) Scanner.Scientific reports · 2022Article
- Translation-Invariant Zero-Phase Wavelet Methods for Feature Extraction in Terahertz Time-Domain Spectroscopy.Sensors (Basel, Switzerland) · 2022Article
- Terahertz Imaging for Breast Cancer Detection.Sensors (Basel, Switzerland) · 2021Review
- Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy.Sensors (Basel, Switzerland) · 2021Review
- Terahertz Imaging and Spectroscopy in Cancer Diagnostics: A Technical Review.BME frontiers · 2020Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues. Furthermore, the principal component analysis (PCA) method and machine learning classifier were further adopted and optimized for automatic classification of the THz signal from breast IDC sample. The areas under the receiver operating characteristic curves are all larger than 0.89 for the three adopted classifiers. The best breast IDC recognition performance is with the precision, sensitivity and specificity of 92.85%, 89.66% and 96.67%, respectively. The results demonstrate the effectiveness of the ESER index together with the machine learning classifier for automatically identifying different breast tissues.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.